Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

Perraudin, Caroline G. M.; Illiano, Vittorio P; Calvo, Francesc; O’Hare, Emer; Donnelly, Seamas C; Mullan, Ronan; Sander, Oliver; Caulfield, Brian; Dorn, Jonas F.

doi:10.1159/000493277

Cited by 22 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Participant demographics are outlined within Table 2. The participants wore the sensors 91.5% of the time on average [24]. Of those recruited, 43 (95.6%), reached the minimum threshold of 80% of the total available analysis days.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t =-4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. Conclusion: This study is the first to show that a

show abstract

Section: Resultsmentioning

confidence: 99%

“…Ethical approval for this study was granted by the Human Ethics Board of the local research institution. The participants and data within this study are the same as those previously reported elsewhere [24].…”

Section: Methodsmentioning

confidence: 99%

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The visualisations were created using activity count data (a threshold-based proxy of movement) previously collected from arthritis patients using an Actigraph GT9X Link (Actigraph LLC), as reported elsewhere [11, 16, 17]. Arthritis patients wore the Actigraph during their usual activities for a period of 28 days.…”

Section: Methodsmentioning

confidence: 99%

“It’s Not as Simple as Just Looking at One Chart”: A Qualitative Study Exploring Clinician’s Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background: Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient’s physical activity patterns. Objective: To explore clinicians’ opinions on different visualisations of actigraphy data. Methods: Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis. Results: Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret. Conclusion: The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.

show abstract

“…However, such hospital-based, controlled imaging studies are not feasible for assessing ongoing real-time objective markers of pain. In contrast, studies using wearable accelerometers to assess pain and function in patients in a free-living environment suggest that objective biomarkers of activity could be developed [70]. Work has also been done to develop objective biosignals for pain in the ICU setting via video monitoring of patient facial expressions for the presence and intensity of pain [71].…”

Section: Pain Assessment: Current Status and The Limits Of Current Outcome Measuresmentioning

confidence: 99%

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

Leroux

Rzasa-Lynn²,

Crainiceanu

et al. 2021

Digit Biomark

View full text Add to dashboard Cite

Introduction: We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain. Methods: We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain. Results: Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited. Conclusion: There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

show abstract

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

Cited by 22 publications

References 33 publications

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

“It’s Not as Simple as Just Looking at One Chart”: A Qualitative Study Exploring Clinician’s Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

Contact Info

Product

Resources

About